Microsoft AI beats Ms. Pac-Man with a perfect score

Is this the start of the AI takeover? Well, not really, not unless the world turned out to be one Ms. Pac-Man game. That’s because Microsoft researchers from its recently acquired Maluuba deep learning startup have developed an AI that was not only able to beat that specific game but was also able to achieve the highest score possible. A score that no human was able to achieve without a cheat.

That said, in a way the Microsoft AI was cheating. To accomplish this herculean task, the AI split the overall goal of beating the game into smaller pieces that the AI then distributed to 150 agents. While it does mimic the way humans use a “divide and conquer” approach to problem solving, our organic brains have nothing on computers’ parallel processing capabilities.

Ms. Pac-Man is definitely not the first computer game to be used as a puzzle for AI to solve. There are already countless examples of that in computer science history. This 1980s arcade game, however, earned the notoriety of having a maximum score of 999,990 that no one was able to reach just by playing the game. Ms. Pac-Man incorporates an element of unpredictability that was deviously designed to keep the quarters flowing inside arcade machines. Nearly four decades later, it has become the perfect testing ground for artificial intelligence.

The researchers dubbed their technique as the Hybrid Reward Architecture, a specialized version of the reinforcement type of learning for AI. Most AI these days use supervised learning, where the AI learns the best path to take based on examples of positive and negative feedback that were fed to it beforehand. Reinforcement learning, in contrast, learns the best way to maximize positive responses through trial and error.

Given how the chaotic nature of Ms. Pac-Man more closely mimics the real world, and how reinforcement learning also more closely mimics how we ourselves learn by trial and error, Microsoft researchers hope that the lessons from Maluuga’s success could be used to develop AI that can help a predict a company’s sales targets, among other executive-related decisions. Managers and executives beware!